According to Globocan 14.1 million new cancer cases occur worldwide of which 3.2 million cases are seen in Europe1. In radiotherapy, response is governed largely by the intrinsic radiosensitivity of the patient, which varies by cancer type. Despite the treatment being standard for around half2 of all cancer patients, the full-response rate to the treatment can be as low as 20% in gastrointestinal cancer3, and 15% in breast cancer4. Adverse side effects from treatment could be reduced if alternatives to radiotherapy could be considered in patients where little to no response is expected.
Each cancer patient has a unique response to treatment. An individual patient's response to cancer treatment is, to a significant degree, determined by their own biology (genetic profile) and other environmental factors such as diet and lifestyle. With the evolution of technologies such as gene sequencing and associated computational methods, it is now possible to link a patient's biological profile and lifestyle characteristics to measurements of their treatment response. This can supply clinicians with a prediction of the risk of recurrence or probability of regression of cancer for a particular patient and for a particular treatment option. The prescription of an individualised therapeutic plan to cancer patients strongly depends on the identification of clinical and biological characteristics that can be used to stratify patients in terms of their probability of therapeutic success5. Researchers have, for the past two decades, attempted to identify clinical and molecular biomarkers for a range of cancer types which can reveal key factors that influence the progression of disease and its resistance to chemotherapeutic or radiotherapeutic treatment6. An additional and parallel aim has been to use biomarkers identified in such studies as potential chemo-therapeutic targets7.
There have been many approaches that have been adopted to satisfy this objective. One has been to analyse expression profiles at a genomic and proteomic level with a view to classification of individual patients into clinical subtypes based on probable response8-10. An alternative approach has been to utilize immunohistochemical imaging approaches with image segregation algorithms to produce standardized measurements of antibody staining profiles11,12 as metrics of patient treatment success. This latter approach has been relatively successful with prognostic performance similar to that of more complex approaches13,14. However, in its totality, these efforts have had limited success in this regard owing to challenges surrounding study design and power15-17, with the result that prognostic markers for many cancer types have not seen widespread adoption and successful use in clinics.
The present invention seeks to alleviate the disadvantages of the prior art in this field. It does so by developing procedures and processes within a test, which allows the objective of prediction of treatment response in cancer patients.
The present method allows the analysis of a variety of sample types including crude biological samples (i.e., not purified or processed or refined and in a natural state). The method can work with samples of blood or blood constituents. The samples may be tissue sections, where whole cells are arranged substantially as they were in their original state. The samples may contain whole cells or cells that have been lysed, either chemically or mechanically. The samples may include cells that are still alive at the time of analysis. The samples may also be fixed or preserved or processed. In some embodiments the samples may be irradiated prior to analysis. The term “prognostic” is used in this specification to mean relating to the prediction of the likely course of a medical condition. This includes the course of the medical condition when treated, including whether the condition improves in response to the treatment and if there are side effects in response to the treatment.
In the description of the invention we refer to “spectral biomarkers”, which requires definition here. A “spectral biomarker” is not the same as a “molecular biomarker”. A spectral biomarker is a region in a spectrum or a pattern in a spectrum obtained from a sample which is correlated with a characteristic of interest. A spectral biomarker is not necessarily indicative of the presence or absence or concentration of any one particular biomolecule. Spectral biomarkers are not typically assigned to any specific biomolecule, in part due to the fact that the biomolecules which impart the characteristics of interest are not observable in crude (i.e., not purified) sample analysis such as vibrational spectroscopy due to the overlapping vibrational spectra of thousands of chemicals; and also because the biomolecules which impart the characteristics of interest may not be explicitly known. These unassigned and often unknown underlying features of the biochemistry of a sample may nonetheless leave a spectral biomarker correlating to a characteristic of interest which is discernible through statistical analysis of the spectra, or “frequent pattern mining”14. These spectral biomarkers may then be used in a triage process.
The present invention relates to the features disclosed in the following numbered statements:    1. A prognostic method of analyzing a biological sample from a cancer patient to predict his/her response to a specified modality of cancer treatment comprising the steps of:            (a) performing spectroscopy on the biological sample to obtain a spectrum;        (b) comparing the obtained spectrum with one or more pre-classified spectra to calculate the probability of a response to the specified modality of cancer treatment by the cancer patient.            2. The method of statement 1 wherein the modality of cancer treatment comprises radiotherapy.    3. The method of statement 1 wherein the modality of cancer treatment comprises radiotherapy combined with either chemotherapy or hormonal therapy.    4. The method of statement 1 wherein the modality of cancer treatment comprises chemotherapy.    5. The method of statement 1 wherein the modality of cancer treatment comprises hormonal therapy.    6. A method according to any one of the preceding statements, wherein the sample is a crude biological sample. In some embodiments the sample does not require chemical processing or fixing. This has the advantage of fewer steps than methods which require processing and thus faster turnover.    7. A method according to any one of the preceding statements, wherein the sample comprises whole cells. These cells may be tumour cells from a tumour biopsy or lymphocytes from a blood sample or exfoliated oral cells for example. The whole cells may also comprise other tissue cells as the spectral biomarkers of radiosensitivity are not necessarily specific to tumour cells.    8. A method according to any one of the preceding statements, wherein the sample comprises live cells. The cells may still be alive during analysis after minimal or no processing as FTIR and Raman spectroscopy are not lethal.    9. A method according to any of the preceding statements wherein the biological sample is not irradiated prior to the spectroscopy analysis. This has the advantage of safety for the person carrying out the test, the method taking less time and thus higher turnover.    10. A method according to any one of the preceding statements, wherein the sample is a tissue sample. This may be a biopsy from a tumour for example.    11. A method according to statement 1, wherein the tissue sample is a formalin fixed-paraffin preserved tissue sample. Fixation preserves the sample and allows the analysis to be done some time after obtaining the sample from the patient. Formalin fixation does not interfere with the spectroscopy or the imaging. The same sample may be used for traditional histology and for Raman or FTIR imaging and regular Raman or FTIR spectroscopy.    12. A method according to statement 11 in which the tissue sample is a microtomed tissue section mounted on a spectroscopic substrate. This ensures the uniformity of samples and the quality of the spectroscopy.    13. A method according to statement 12 in which the tissue sample is a 10 μm thick tissue section mounted on a spectroscopic substrate.    14. A method according to any one of statements 1 to 9, wherein the sample comprises a biofluid including blood; blood constituents; and also other biofluids including, but not limited to urine, saliva. Such biofluid samples have the advantage of being easily obtained from a patient.
Furthermore, the sample for use in the prognostic method of the present invention may comprise a whole blood sample. This is significantly advantageous for ease of use of the prognostic method of the present invention.    15. A method according to any one of statements 1 to 9, wherein the sample is contained within a microwell plate. The sample may be used in other forms of biological testing and analysis subsequently.    16. A method according to any one of statements 1 to 9, wherein the sample is a blood lymphocyte sample. This has the advantage over whole blood of not having red blood cells, which have vibrational spectra which may mask or overlap with certain spectral biomarkers in certain embodiments.    17. A method according to any one of the preceding statements wherein the spectroscopy is vibrational spectroscopy. This has the advantages of being cost-effective, fast, suitable for mixtures, quantitative, suitable for imaging and that it does not damage the sample or require extensive processing of the sample. The sample may subsequently be used for other forms of analysis.    18. A method according to statement 17 wherein the vibrational spectroscopy is performed using Raman spectroscopy. This has the advantages of being cost-effective, fast, suitable for mixtures, quantitative, suitable for imaging and also that it does not damage the sample or require extensive processing of the sample. The sample may subsequently be used for other forms of analysis.    19. A method according to statement 17 wherein the vibrational spectroscopy is performed using FTIR spectroscopy. This has the advantages of being cost-effective, fast, suitable for mixtures, quantitative, suitable for imaging and that it does not damage the sample or require extensive processing of the sample. The sample may subsequently be used for other forms of analysis.    20. A method according to statement 17 wherein the vibrational spectroscopy is performed using FTIR imaging. The imaging may be used as an adjunct to the spectral analysis. For example, in one embodiment the FTIR imaging is used to select a point or group of points on the image form which a useful FTIR spectrum for analysis may be obtained. The imaging may additionally be used as part of a histological analysis of a tumour.    21. A method according to statement 17 wherein the vibrational spectroscopy is performed using Raman imaging. The imaging may be used as an adjunct to the spectral analysis. For example, in one embodiment the Raman imaging is used to select a point or group of points on the image form which a useful Raman spectrum for analysis may be obtained. The imaging may additionally be used as part of a histological analysis of a tumour.    22. The method of statement 1 wherein the response to cancer treatment comprises tumour regression.    23. The method of statement 22 wherein the response to cancer treatment comprises complete tumour regression.    24. The method of statement 22 wherein the response to cancer treatment comprises partial tumour regression.    25. The method of statement 22 wherein the response to cancer treatment comprises intermediate tumour regression.    26. The method of any preceding statement wherein the response to cancer treatment comprises unwanted side effects.    27. The method of statement 26 wherein the response to cancer treatment comprises radiotherapeutic treatment toxicity.    28. The method of statement 26 wherein the response to cancer treatment comprises chemotherapeutic treatment toxicity.    29. A method according to statement 1, wherein the step of comparing comprises the use of spectral decomposition followed by analysis by a classifier.    30. A method according to statement 29, wherein the spectral decomposition comprises the use of principal component analysis (PCA) spectral decomposition.    31. A method according to statement 29, wherein the classifier is a Linear Discriminant analysis classifier.    32. A method according to statement 31, wherein the Linear Discriminant analysis classifier is a Fisher's Linear Discriminant classifier.    33. A method according to statement 29, wherein the classifier is a Quadratic Discriminant analysis classifier.    34. A method according to statement 33, wherein the Quadratic Discriminant analysis classifier is a Fisher's Quadratic Discriminant classifier.    35. A method according to statement 29, wherein the classifier is a support vector machine classifier.    36. A method according to statement 29, wherein the classifier is a decision tree classifier.    37. A method according to statement 29, wherein the classifier is a neural network.    38. A method according to statement 14, further comprising the step of culturing the blood lymphocyte cells as whole blood in-vitro.    39. A method according to statement 14, further comprising the step of irradiating the in-vitro blood sample. This allows the detection of certain changes that occur upon irradiation of the sample. An irradiated sample may be compared to a non-irradiated sample in some embodiments.    40. A method according to statement 32, further comprising extracting lymphocytes from the irradiated sample.    41. A method according to statement 34, further comprising the step of fixing the lymphocytes and wherein the step of performing spectroscopy is carried out on the fixed lymphocyte material.    42. A method according to any preceding statement, wherein the cancer is oesophageal cancer.    43. A method according to any preceding statement, wherein the cancer is colorectal cancer.    44. A method according to any one of statements 1 to 42, wherein the cancer is prostate cancer.    45. A method according to any one of statements 1 to 35, wherein the cancer is breast cancer.    46. A method according to any of the preceding statements wherein the cancer patient is a mammal.    47. A method according to any of the preceding statements wherein the cancer patient is a human.    48. A method according to statement 26 wherein the response to cancer treatment comprises normal tissue toxicity.    49. A method according to statement 26 wherein the adverse effects are classified according to standard toxicity scoring systems such as CTCAE or RTOG or similar.
In cases where the cancer comprises a solid tumour and a biopsy of the tumour has been taken, the following biopsy and imaging method steps are carried out:                A. obtaining diagnosis of a cancer patient;        B. obtaining sample via tumour biopsy;        C. subjecting sample to fixation and paraffin embedding;        D. performing microtoming on sample;        E. acquiring FTIR/Raman spectra;        F. analysing obtained images by a statistical learning algorithm which compares obtained spectra to spectra from a pre-classified library; and        G. predicting response to therapy.        
The step of acquiring FTIR or Raman imaging of the sample may comprise obtaining spectra from many points in the sample. The Acquisition step (imaging) may comprise histological analysis and selection of a point or group of points on the sample from which the vibrational spectra will be analysed in later steps.
In cases where a blood sample is taken from a patient, the blood sample may be used whole and analysed or the plasma may be extracted and analysed or the lymphocytes may be extracted and analysed.
The steps for analysing plasma are as follows:                i. obtaining diagnosis of a cancer patient;        ii. obtaining blood sample;        iii. extracting serum or plasma;        iv. depositing serum or plasma on substrate;        v. acquiring Raman/FTIR spectra;        vi. analysing obtained spectra by a statistical learning algorithm which compares obtained spectra to spectra from a pre-classified library; and        vii. predicting response to therapy.        
The steps for analysing lymphocytes are as follows:                a. obtaining diagnosis of a cancer patient;        b. obtaining blood sample;        c. extracting lymphocytes;        d. fixing lymphocytes;        e. depositing fixed lymphocytes on substrate;        f. acquiring Raman/FTIR spectra;        g. analysing obtained spectra by a statistical learning algorithm which compares obtained spectra to spectra from a pre-classified library; and        h. predicting response to therapy.        
Optionally, the above method of analysing lymphocytes may comprise the additional step of isolating peripheral blood mononuclear cells (PBMC), carried out after step (b). Other blood components may be present during analysis.
The method may also comprise the step of subjecting whole blood sample to in-vitro gamma-irradiation; in which the lymphocytes are given a challenge dose of gamma radiation is optional. The lymphocytes may be analysed without in-vitro gamma-irradiation.
In accordance with statement 1 above, the present application has the advantage of providing a test having high specificity and sensitivity that estimates the probability of response to treatment for a wide range of cancers. Thus, two alternative approaches may be used in accordance with the method of the present invention as will become further apparent from the following disclosure.